DRBRN: A Deep Reconstruction Bottleneck Representation Network for CT-Image-Based Pancreatic Cancer Diagnosis in Internet of Medical Things

Automatic classification and survival prediction of pancreatic cancer based on computed tomography (CT) images have become a key research direction in the field of intelligent healthcare. Various deep learning-based diagnostic models have been developed for classifying pancreatic cancer from CT images. However, CT images often contain noise and a significant amount of irrelevant information unrelated to pancreatic cancer diagnosis. This irrelevant information weakens the discriminative power of the extracted features, leading to the problem of information redundancy. In addition, existing models generally suffer from poor feature generalization. To address these challenges, we propose a Deep Reconstruction Bottleneck Representation Network (DRBRN) for pancreatic cancer diagnosis, based on Internet of Medical Things (IoMT) and CT imaging. In essence, the DRBRN leverages the information bottleneck principle and a reconstruction task to learn more compact and generalizable features from CT images. It effectively filters out redundant information and noise, preserving only the critical features relevant to the diagnosis of pancreatic cancer. The experimental results on real-world pancreatic cancer CT dataset demonstrate that our proposed DRBRN model achieved the best performance across all three metrics, with a Precision of 0.996, Accuracy of 0.995, and F1-Score of 0.995.